Research on APP Intelligent Promotion Decision Aiding System Based on Python Data Analysis and AARRR Model

Abstract

With the development of AI technology and the current situation of the epidemic, online shopping is increasingly popular all over the world. The mobile phone terminals also occupy a larger share, and the use of various APPs has also emerged rapidly in line with the trend of the times. How big data technology is used to analyze the effective data generated by APPs? The paper conducts research from the following three aspects: (1)Finding out customer's behavior patterns from the analyzed e-commerce data, and analyzing the reasons for customer churn rate and providing effective marketing strategies for merchants; (2) according to the time dimension, from different time dimension of customer behavior rules, find out customer purchase rules for merchants; (3) from the point of view of commodities, finding out customers' preferences for different commodities, and give different marketing schemes for commodities with different preferences. Through the three aspects of the study, we use Python for data analysis, combined with AARRR model and traditional expert system, the arctic finally sets up an intelligent decision aid system recommended by APP, the system provides different marketing schemes for merchants. The experiment shows that through data analysis and simulation of the use of an APP, in the experimental environment, the APP can better serve customers under the same conditions, and the transaction rate has increased by 7.3%.